Helmy, M., Elhalis, H., Liu, Y., Chow, Y., & Selvarajoo, K. (2023). Perspective: Multiomics and Machine Learning Help Unleash the Alternative Food Potential of Microalgae. Advances in Nutrition, 14(1), 1–11. https://doi.org/10.1016/j.advnut.2022.11.002
Abstract:
Food security is a pressing challenge that faces the world. The ever-increasing world population, ongoing COVID-19 pandemic, political conflicts together with climate change issues make the problem very serious. Therefore, fundamental changes to the current food system and new sources of alternative food are required. Recently, the exploration of alternative food sources has been supported by numerous governmental and research organizations, as well as small and large commercial ventures. Microalgae are gaining momentum as an effective source for alternative lab-based nutritional proteins as they are easy to grow under variable environmental conditions, with the added ability to absorb carbon dioxide. Despite their attractiveness, the utilization of microalgae faces several practical limitations. Here we discuss both the potential and challenges of microalgae in food sustainability, and its possible long-term contribution to circular economy converting food waste into modern feed methods. We also argue systems biology and artificial intelligence (AI) can play a role in overcoming some of the challenges and limitations, through data-guided metabolic flux optimization and systemic increasing growth of the microalgae strains without negative outcomes such as toxicity. This requires microalgae databases rich in omics data, and further developments on its mining and analytics methods.
License type:
Attribution 4.0 International (CC BY 4.0)
Funding Info:
This research / project is supported by the A*STAR - Singapore Food Story R&D Programme (1st Alternative Protein Seed Challenges)
Grant Reference no. : W20W2D0017